Lecture 3 - Donald Bren School of Information and Computer

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Lecture 3

Uninformed Search

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Uninformed search strategies

  Uninformed: While searching you have no clue whether one non-goal state is better than any other. Your search is blind. You don’t know if your current exploration is likely to be fruitful.

  Various blind strategies:   Breadth-first search   Uniform-cost search   Depth-first search   Iterative deepening search

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Breadth-first search

  Expand shallowest unexpanded node   Fringe: nodes waiting in a queue to be explored

  Implementation:   fringe is a first-in-first-out (FIFO) queue, i.e.,

new successors go at end of the queue.

Is A a goal state?

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  Expand shallowest unexpanded node   Implementation:

  fringe is a FIFO queue, i.e., new successors go at end

Expand: fringe = [B,C]

Is B a goal state?

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  Expand shallowest unexpanded node

  Implementation:   fringe is a FIFO queue, i.e., new successors go

at end Expand: fringe=[C,D,E]

Is C a goal state?

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  Expand shallowest unexpanded node   Implementation:

  fringe is a FIFO queue, i.e., new successors go at end

Expand: fringe=[D,E,F,G]

Is D a goal state?

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Example BFS

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Properties of breadth-first search

  Complete? Yes it always reaches goal (if b is finite)   Time? 1+b+b2+b3+… +bd + (bd+1-b)) = O(bd+1) (this is the number of nodes we generate)   Space? O(bd+1) (keeps every node in memory, either in fringe or on a path to fringe).   Optimal? Yes (if we guarantee that deeper

solutions are less optimal, e.g. step-cost=1).

  Space is the bigger problem (more than time)

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Uniform-cost search

Breadth-first is only optimal if step costs is increasing with depth (e.g. constant). Can we guarantee optimality for any step cost?

Uniform-cost Search: Expand node with smallest path cost g(n).

Proof Completeness:

Given that every step will cost more than 0, and assuming a finite branching factor, there is a finite number of expansions required before the total path cost is equal to the path cost of the goal state. Hence, we will reach it.

Proof of optimality given completeness:

Assume UCS is not optimal. Then there must be an (optimal) goal state with path cost smaller than the found (suboptimal) goal state (invoking completeness). However, this is impossible because UCS would have expanded that node first by definition. Contradiction.

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Uniform-cost search

Implementation: fringe = queue ordered by path cost Equivalent to breadth-first if all step costs all equal.

Complete? Yes, if step cost ≥ ε (otherwise it can get stuck in infinite loops)

Time? # of nodes with path cost ≤ cost of optimal solution.

Space? # of nodes with path cost ≤ cost of optimal solution.

Optimal? Yes, for any step cost ≥ ε

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S B

A D

E

C

F

G

1 20

2

3

4 8

6 1 1

The graph above shows the step-costs for different paths going from the start (S) to the goal (G).

Use uniform cost search to find the optimal path to the goal.

Exercise for at home

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Depth-first search

  Expand deepest unexpanded node   Implementation:

  fringe = Last In First Out (LIPO) queue, i.e., put successors at front

Is A a goal state?

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Depth-first search


  fringe = LIFO queue, i.e., put successors at front

queue=[B,C]

Is B a goal state?

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Depth-first search



queue=[D,E,C]

Is D = goal state?

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Depth-first search



queue=[H,I,E,C]

Is H = goal state?

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Depth-first search



queue=[I,E,C]

Is I = goal state?

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Depth-first search



queue=[E,C]

Is E = goal state?

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Depth-first search



queue=[J,K,C]

Is J = goal state?

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Depth-first search



queue=[K,C]

Is K = goal state?

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Depth-first search



queue=[C]

Is C = goal state?

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Depth-first search



queue=[F,G]

Is F = goal state?

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Depth-first search



queue=[L,M,G]

Is L = goal state?

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Depth-first search



queue=[M,G]

Is M = goal state?

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Properties of depth-first search

  Complete? No: fails in infinite-depth spaces Can modify to avoid repeated states along path

  Time? O(bm) with m=maximum depth   terrible if m is much larger than d

  but if solutions are dense, may be much faster than breadth-first

  Space? O(bm), i.e., linear space! (we only need to remember a single path + expanded unexplored nodes)

  Optimal? No (It may find a non-optimal goal first)

A

B C

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Iterative deepening search

•  To avoid the infinite depth problem of DFS, we can decide to only search until depth L, i.e. we don’t expand beyond depth L. Depth-Limited Search

•  What if solution is deeper than L? Increase L iteratively. Iterative Deepening Search

•  As we shall see: this inherits the memory advantage of Depth-First search, and is better in terms of time complexity than Breadth first search.

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Iterative deepening search L=0

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Iterative Deepening Search L=3

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Iterative deepening search

  Number of nodes generated in a depth-limited search to depth d with branching factor b:

NDLS = b0 + b1 + b2 + … + bd-2 + bd-1 + bd

  Number of nodes generated in an iterative deepening search to depth d with branching factor b:

NIDS = (d+1)b0 + d b1 + (d-1)b2 + … + 3bd-2 +2bd-1 + 1bd =

  For b = 10, d = 5,   NDLS = 1 + 10 + 100 + 1,000 + 10,000 + 100,000 = 111,111   NIDS = 6 + 50 + 400 + 3,000 + 20,000 + 100,000 = 123,450   NBFS = ............................................................................................ = 1,111,100

BFS

Note: BFS can also be adapted to be by waiting to expand until all nodes at depth d are checked

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Properties of iterative deepening search

  Complete? Yes   Time? O(bd)   Space? O(bd)   Optimal? Yes, if step cost = 1 or increasing

function of depth.

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Bidirectional Search

  Idea   simultaneously search forward from S and backwards

from G   stop when both “meet in the middle”   need to keep track of the intersection of 2 open sets of

nodes

  What does searching backwards from G mean   need a way to specify the predecessors of G

  this can be difficult,   e.g., predecessors of checkmate in chess?

  which to take if there are multiple goal states?   where to start if there is only a goal test, no explicit list?

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Bi-Directional Search Complexity: time and space complexity are:

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Summary of algorithms

even complete if step cost is not increasing with depth.

preferred uninformed search strategy

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Repeated states

  Failure to detect repeated states can turn a linear problem into an exponential one!

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Solutions to Repeated States

  Graph search   never generate a state generated before

  must keep track of all possible states (uses a lot of memory)   e.g., 8-puzzle problem, we have 9! = 362,880 states   approximation for DFS/DLS: only avoid states in its (limited)

memory: avoid looping paths.   Graph search optimal for BFS and UCS, not for DFS.

S

B

C

S

B C

S C B S

State Space Example of a Search Tree

optimal but memory inefficient

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Summary

  Problem formulation usually requires abstracting away real-world details to define a state space that can feasibly be explored

  Variety of uninformed search strategies

  Iterative deepening search uses only linear space and not much more time than other uninformed algorithms

http://www.cs.rmit.edu.au/AI-Search/Product/ http://aima.cs.berkeley.edu/demos.html (for more demos)

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2. Consider the graph below:

a) [2pt] Draw the first 3 levels of the full search tree with root node given by A. b) [2pt] Give an order in which we visit nodes if we search the tree breadth first. c) [2pt] Express time and space complexity for general breadth-first search in terms

of the branching factor, b, and the depth of the goal state, d. d) [2pt] If the step-cost for a search problem is not constant, is breadth first search

always optimal? (Explain).

A D B F

C E

Exercise

Lecture 3 - Donald Bren School of Information and Computer

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Transcript of Lecture 3 - Donald Bren School of Information and Computer